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In silico medicine opens us office, prepares to launch drug discovery software for aging research market bio inform _ informatics _ genomeweb
1. 4/2/14 In Silico Medicine Opens US Office, Prepares to Launch Drug Discovery Software for Aging Research Market | BioInform | Informatics | GenomeWeb
www.genomeweb.com/informatics/silico-medicine-opens-us-office-prepares-launch-drug-discovery-software-aging-re 1/3
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In Silico Medicine Opens US Office, Prepares to Launch
Drug Discovery Software for Aging Research Market
March 28, 2014
By Uduak Grace Thomas
In Silico Medicine, a newly minted bioinformatics company, is
hoping to build a business by providing software that supports
research focused on drugs that have the potential to slow or
reverse agerelated processes.
The company, which officially opened its doors earlier this month, has set up shop in
Baltimore and will soon begin marketing a product called Geroscope. With this
product, In Silico is targeting the pharmaceutical industry and large clinical institutions.
The company is positioning its offering as a computational platform for screening and
predicting the effectiveness of drugs that suppress agingrelated processes. The
system works by comparing differences in gene expression and signaling pathway
data collected from cells in different tissues in older individuals versus those taken
from younger healthy individuals. It then looks for drugs that can stimulate the
pathways in older cells to behave as they do in the younger cells. It's currently being
validated in cell lines and model organisms with a full launch planned for later this
year.
Geroscope is based on similar technological concepts that underlie a system called
OncoFinder, which is used to select and rank personalized cancer therapies. That
platform is owned and used internally by Pathway Pharmaceuticals, a Hong Kong
based pharma company. In Silico Medicine licenses the technology from them.
Alex Zhavoronkov, In Silico Medicine's CEO, was one of OncoFinder's developers.
Explaining how the technology came to be used at Pathway and now at his company,
he told BioInform that the process began with research projects where he and others
were analyzing gene expression data collected from patients with tumors. They
developed the computational methods, he said, of mapping gene expression data
onto various cellular pathways that are activated in cancer cases, and for ranking
drugs that targeted these pathways and successfully killed the cells gone wrong or
sent them into a state that was as close to normal as possible. They used the system
to analyze data from various solid and bloodbased tumors including bladder cancer,
head and neck cancer, and leukemia.
After validating the algorithms that would eventually comprise the OncoFinder
software in almost 1,000 cancer patients, Zhavoronkov and colleagues
commercialized the technology first in Russia providing it as a service to oncologists
there who were looking for more guidance in selecting treatments for their patients,
and then they expanded the technology to China where Pathway Pharma used it in
their drug discovery efforts. "After that we realized this particular personalized
medicine approach can be used for drug discovery for aging research," he told
BioInform. "We could take gene expression data from the 'young cell' and look at the
signaling pathway activation profile that is characteristic to that tissue and to that
specific age of the patient" and then compare it to the same sort of data collected from
cells in older individuals.
The key to the technology, and what makes it a cut above similar systems, according
to Zhavoronkov, is that "we found the fine line between too much complexity and too
little complexity." Some groups have "tried to rank targeted compounds by looking
essentially at every single genome in [a given] network, [but they can't] really find good
correlations from patient to patient and from norm to cancer."
Instead of looking at all possible pathways in cells, "we identified a limited number of
In this issue of BioInform
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In Silico Medicine Opens US Office,
Prepares to Launch Drug Discovery
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UWMadison Selects Cartagenia's Bench
Platform for Genomic Analysis
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Michael Stitzel
The Jackson Laboratory
Epigenome in Type
2 Diabetes
Michael Stitzel joked that
his research has led him
to climb up the
evolutionary tree. As an
undergraduate he worked
with yeast, and over the
years, he has worked his
way up to studying
Drosophila, C. elegans, and, now, people.
Stitzel shifted from the more basic roots of the tree to
the biomedical limbs as a matter of motivation. He said
that as a graduate student, though he worked on an
important and interesting question, when experiments
didn't go well, it was hard for him to get motivated. And
so, he found himself drawn to questions with more
direct relevance to medicine.
He turned to studying type 2 diabetes. Using a genome
wide association study approach, he identified a
number of regions in the genome linked to the disease,
but many of them were, as he put it, "in the middle of
nowhere." This led him to thinking about epigenetics,
and then to later uncover what he and his colleagues
dubbed 'stretch enhancers,' longerthanaverage
enhancers that appear to be driving physiological
functions.
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2. 4/2/14 In Silico Medicine Opens US Office, Prepares to Launch Drug Discovery Software for Aging Research Market | BioInform | Informatics | GenomeWeb
www.genomeweb.com/informatics/silico-medicine-opens-us-office-prepares-launch-drug-discovery-software-aging-re 2/3
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pathways. So, for cancer, we look at pathways that are related to cancer," for example,
some of the metabolic and cell cycle pathways for a total of just over 100 pathways.
"That is easier at looking at a [large dataset] with unrelated elements, which is an
approach that some groups have taken," he said. They also look at what they believe
is an appropriate number of elements per pathway — about 65 to 70 elements on
average, he said. "It's much more accurate and more effective than looking at a very
small [number of] individual network elements," for example, just 10 or 15 elements.
In Silico Medicine has finished developing its version of the software — which
includes some new capabilities in addition to those that are in its source software —
and it is now validating the tool in human fibroblasts and nematodes. The company
also constructed internal databases of gene expression data from young healthy cells
in a variety of tissues that it has gleaned from both opensource and commercial
repositories as well as various drug compounds with known molecular targets. The
validation tests are being done in conjunction with laboratories in academic
institutions here in the US, China, and Russia, Zhavoronkov said. They plan to publish
the results of these tests this in July, after which they will begin making the software
available for use in clinical contexts.
Zhavoronkov said that the company hopes to begin offering Geroscope for clinical use
either just before or during an Aging Research Symposium at this year's MipTec
conference — a drug discovery and life sciences research event that will be held in
Basel, Switzerland on Sept. 23 – 25. Currently the software is available for research
use. Customers that fall into this category — academic research institutions mostly —
don’t have to pay a licensing fee. Rather the company forms research collaborations
with these clients where it provides access to its servers and expertise and works in
concert to analyze the data these groups generate. In return, it receives a share of the
proceeds of any intellectual property that's generated over the course of the project.
Zhavoronkov didn't disclose the exact percentage. Its pharmaceutical clients on the
other hand pay an undisclosed "milestone payment," Zhavoronkov said, to access to
company's software and expertise.
In Silico Medicine sees a number of potential areas where its software could be
useful. Besides helping researchers identify novel treatments, Geroscope could help
pharma customers identify and rank potentially useful geroprotective drugs from lists
of approved treatements, Zhavoronkov said. "We can take a very large number of
drugs with known molecular targets, run it through Geroscope and rank them placing
those known to have beneficial effects on agerelated processes higher up on the
totem pole.
Another benefit of the system is its ability to help clients tailor treatments to individuals.
"We know there could be similar phenotypes in people that age, but we all age at very
different rates and for very different reasons … and we all metabolize drugs
differently," Zhavoronkov said. "A system which helps you … predict the efficacy of a
combination of drugs, some of which may already be approved for various therapeutic
reasons for various conditions, is much more valuable than having a general one pill
fits all strategy."
Finally, Geroscope could help pharma companies select participants who will derive
the most benefit from clinical trials. Since "most drugs will not work in two different
patients in exactly the same way, we will be able to help predict the outcome of
[applying] various therapies on specific patients" prior to enrolling that patient in a
clinical trial, he said.
Uduak Grace Thomas is the editor of GenomeWeb's BioInform.
She covers bioinformatics, computational biology, and life science
informatics. Email Uduak Grace Thomas or follow her
GenomeWeb Twitter account at @BioInformGW.
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